Overview

Dataset statistics

Number of variables23
Number of observations1126
Missing cells1143
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory194.8 KiB
Average record size in memory177.1 B

Variable types

Text4
Unsupported7
Numeric7
Categorical2
DateTime2
Boolean1

Alerts

GitHub Stars is highly overall correlated with GitHub Forks and 3 other fieldsHigh correlation
GitHub Forks is highly overall correlated with GitHub Stars and 3 other fieldsHigh correlation
GitHub Watchers is highly overall correlated with GitHub Stars and 3 other fieldsHigh correlation
GitHub Open Issues is highly overall correlated with GitHub Stars and 3 other fieldsHigh correlation
GitHub Contributors is highly overall correlated with GitHub Stars and 3 other fieldsHigh correlation
GitHub Repo Archived is highly imbalanced (90.9%)Imbalance
category is highly imbalanced (75.0%)Imbalance
Project Homepage has 538 (47.8%) missing valuesMissing
GitHub License Type has 551 (48.9%) missing valuesMissing
GitHub Description has 54 (4.8%) missing valuesMissing
GitHub Stars is highly skewed (γ1 = 21.54962409)Skewed
GitHub Forks is highly skewed (γ1 = 25.30007266)Skewed
GitHub Watchers is highly skewed (γ1 = 22.76499067)Skewed
Project Landscape Category is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Topics is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Detected Languages is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Most Recent Commit is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Created to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
Negative Duration Most Recent Commit to Now is an unsupported type, check if it needs cleaning or further analysisUnsupported
GitHub Stars has 44 (3.9%) zerosZeros
GitHub Forks has 434 (38.5%) zerosZeros
GitHub Watchers has 52 (4.6%) zerosZeros
GitHub Open Issues has 675 (59.9%) zerosZeros
GitHub Contributors has 27 (2.4%) zerosZeros

Reproduction

Analysis started2023-10-16 14:06:37.568003
Analysis finished2023-10-16 14:06:43.105771
Duration5.54 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Distinct1090
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:43.376300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length100
Median length65
Mean length15.011545
Min length3

Characters and Unicode

Total characters16903
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1062 ?
Unique (%)94.3%

Sample

1st rowpandas
2nd rownumpy
3rd rowarrow
4th rowduckdb
5th rowparquet-mr
ValueCountFrequency (%)
single-cell-analysis 8
 
0.7%
single-cell-rna-seq-analysis 7
 
0.6%
single_cell_analysis 7
 
0.6%
singlecellanalysis 4
 
0.4%
single-cell-rna-seq 4
 
0.4%
singlecell 3
 
0.3%
orchestratingsinglecellanalysis 3
 
0.3%
single-cell-data-analysis 2
 
0.2%
scrna-tools 2
 
0.2%
sctools 2
 
0.2%
Other values (1060) 1084
96.3%
2023-10-16T08:06:43.687518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1439
 
8.5%
l 1340
 
7.9%
s 1263
 
7.5%
i 1027
 
6.1%
a 1010
 
6.0%
n 950
 
5.6%
- 848
 
5.0%
c 782
 
4.6%
o 696
 
4.1%
t 619
 
3.7%
Other values (55) 6929
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12375
73.2%
Uppercase Letter 2820
 
16.7%
Dash Punctuation 848
 
5.0%
Connector Punctuation 429
 
2.5%
Decimal Number 411
 
2.4%
Other Punctuation 20
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1439
11.6%
l 1340
10.8%
s 1263
10.2%
i 1027
 
8.3%
a 1010
 
8.2%
n 950
 
7.7%
c 782
 
6.3%
o 696
 
5.6%
t 619
 
5.0%
r 572
 
4.6%
Other values (16) 2677
21.6%
Uppercase Letter
ValueCountFrequency (%)
A 427
15.1%
C 394
14.0%
S 374
13.3%
R 232
 
8.2%
N 205
 
7.3%
M 133
 
4.7%
T 129
 
4.6%
P 128
 
4.5%
I 113
 
4.0%
D 104
 
3.7%
Other values (16) 581
20.6%
Decimal Number
ValueCountFrequency (%)
2 156
38.0%
0 99
24.1%
1 71
17.3%
3 22
 
5.4%
9 21
 
5.1%
8 14
 
3.4%
4 11
 
2.7%
7 8
 
1.9%
6 5
 
1.2%
5 4
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 848
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 429
100.0%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15195
89.9%
Common 1708
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1439
 
9.5%
l 1340
 
8.8%
s 1263
 
8.3%
i 1027
 
6.8%
a 1010
 
6.6%
n 950
 
6.3%
c 782
 
5.1%
o 696
 
4.6%
t 619
 
4.1%
r 572
 
3.8%
Other values (42) 5497
36.2%
Common
ValueCountFrequency (%)
- 848
49.6%
_ 429
25.1%
2 156
 
9.1%
0 99
 
5.8%
1 71
 
4.2%
3 22
 
1.3%
9 21
 
1.2%
. 20
 
1.2%
8 14
 
0.8%
4 11
 
0.6%
Other values (3) 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1439
 
8.5%
l 1340
 
7.9%
s 1263
 
7.5%
i 1027
 
6.1%
a 1010
 
6.0%
n 950
 
5.6%
- 848
 
5.0%
c 782
 
4.6%
o 696
 
4.1%
t 619
 
3.7%
Other values (55) 6929
41.0%

Project Homepage
Text

MISSING 

Distinct206
Distinct (%)35.0%
Missing538
Missing (%)47.8%
Memory size8.9 KiB
2023-10-16T08:06:43.922423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length116
Median length0
Mean length13.770408
Min length0

Characters and Unicode

Total characters8097
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)34.4%

Sample

1st rowhttps://pandas.pydata.org
2nd rowhttps://numpy.org
3rd rowhttps://arrow.apache.org/
4th rowhttp://www.duckdb.org
5th row
ValueCountFrequency (%)
http://scvi-tools.org 2
 
1.0%
https://scanpy.readthedocs.io 2
 
1.0%
http://nasqar.abudhabi.nyu.edu 2
 
1.0%
https://arc85.github.io/celltalker 1
 
0.5%
https://combine-lab.github.io/salmon 1
 
0.5%
https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome 1
 
0.5%
https://biofam.github.io/mofa2 1
 
0.5%
https://pydance.readthedocs.io 1
 
0.5%
https://arrow.apache.org 1
 
0.5%
http://www.duckdb.org 1
 
0.5%
Other values (195) 195
93.8%
2023-10-16T08:06:44.235574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 763
 
9.4%
/ 732
 
9.0%
s 494
 
6.1%
i 477
 
5.9%
o 466
 
5.8%
e 415
 
5.1%
. 411
 
5.1%
h 409
 
5.1%
a 368
 
4.5%
p 311
 
3.8%
Other values (55) 3251
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6112
75.5%
Other Punctuation 1349
 
16.7%
Decimal Number 291
 
3.6%
Uppercase Letter 201
 
2.5%
Dash Punctuation 125
 
1.5%
Connector Punctuation 17
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 763
12.5%
s 494
 
8.1%
i 477
 
7.8%
o 466
 
7.6%
e 415
 
6.8%
h 409
 
6.7%
a 368
 
6.0%
p 311
 
5.1%
c 296
 
4.8%
r 285
 
4.7%
Other values (16) 1828
29.9%
Uppercase Letter
ValueCountFrequency (%)
C 29
14.4%
S 27
13.4%
A 23
11.4%
R 16
 
8.0%
M 11
 
5.5%
T 10
 
5.0%
D 9
 
4.5%
I 9
 
4.5%
O 9
 
4.5%
N 9
 
4.5%
Other values (12) 49
24.4%
Decimal Number
ValueCountFrequency (%)
1 63
21.6%
0 58
19.9%
2 42
14.4%
3 23
 
7.9%
4 22
 
7.6%
6 19
 
6.5%
8 17
 
5.8%
9 16
 
5.5%
7 16
 
5.5%
5 15
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/ 732
54.3%
. 411
30.5%
: 206
 
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 125
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6313
78.0%
Common 1784
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 763
 
12.1%
s 494
 
7.8%
i 477
 
7.6%
o 466
 
7.4%
e 415
 
6.6%
h 409
 
6.5%
a 368
 
5.8%
p 311
 
4.9%
c 296
 
4.7%
r 285
 
4.5%
Other values (38) 2029
32.1%
Common
ValueCountFrequency (%)
/ 732
41.0%
. 411
23.0%
: 206
 
11.5%
- 125
 
7.0%
1 63
 
3.5%
0 58
 
3.3%
2 42
 
2.4%
3 23
 
1.3%
4 22
 
1.2%
6 19
 
1.1%
Other values (7) 83
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8097
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 763
 
9.4%
/ 732
 
9.0%
s 494
 
6.1%
i 477
 
5.9%
o 466
 
5.8%
e 415
 
5.1%
. 411
 
5.1%
h 409
 
5.1%
a 368
 
4.5%
p 311
 
3.8%
Other values (55) 3251
40.2%
Distinct1123
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:44.457515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length157
Median length92
Mean length45.162522
Min length28

Characters and Unicode

Total characters50853
Distinct characters67
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1120 ?
Unique (%)99.5%

Sample

1st rowhttps://github.com/pandas-dev/pandas
2nd rowhttps://github.com/numpy/numpy
3rd rowhttps://github.com/apache/arrow
4th rowhttps://github.com/duckdb/duckdb
5th rowhttps://github.com/apache/parquet-mr
ValueCountFrequency (%)
https://github.com/scverse/scvi-tools 2
 
0.2%
https://github.com/scverse/scanpy 2
 
0.2%
https://github.com/shians/cellbench 2
 
0.2%
https://github.com/lmassier/hwat_singlecell 1
 
0.1%
https://github.com/qupath/qupath 1
 
0.1%
https://github.com/apache/parquet-mr 1
 
0.1%
https://github.com/snakemake/snakemake 1
 
0.1%
https://github.com/satijalab/seurat 1
 
0.1%
https://github.com/napari/napari 1
 
0.1%
https://github.com/milaboratory/mixcr 1
 
0.1%
Other values (1113) 1113
98.8%
2023-10-16T08:06:44.768092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4504
 
8.9%
t 4414
 
8.7%
i 3038
 
6.0%
s 2952
 
5.8%
h 2817
 
5.5%
o 2446
 
4.8%
c 2311
 
4.5%
e 2218
 
4.4%
a 2210
 
4.3%
l 1918
 
3.8%
Other values (57) 22025
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38064
74.9%
Other Punctuation 6776
 
13.3%
Uppercase Letter 3741
 
7.4%
Dash Punctuation 1106
 
2.2%
Decimal Number 737
 
1.4%
Connector Punctuation 429
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4414
 
11.6%
i 3038
 
8.0%
s 2952
 
7.8%
h 2817
 
7.4%
o 2446
 
6.4%
c 2311
 
6.1%
e 2218
 
5.8%
a 2210
 
5.8%
l 1918
 
5.0%
g 1820
 
4.8%
Other values (16) 11920
31.3%
Uppercase Letter
ValueCountFrequency (%)
A 476
12.7%
C 465
12.4%
S 452
12.1%
R 256
 
6.8%
N 238
 
6.4%
M 194
 
5.2%
L 173
 
4.6%
T 173
 
4.6%
I 157
 
4.2%
P 157
 
4.2%
Other values (16) 1000
26.7%
Decimal Number
ValueCountFrequency (%)
2 196
26.6%
0 152
20.6%
1 126
17.1%
9 54
 
7.3%
3 47
 
6.4%
8 43
 
5.8%
4 39
 
5.3%
7 31
 
4.2%
5 27
 
3.7%
6 22
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 4504
66.5%
. 1146
 
16.9%
: 1126
 
16.6%
Dash Punctuation
ValueCountFrequency (%)
- 1106
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41805
82.2%
Common 9048
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4414
 
10.6%
i 3038
 
7.3%
s 2952
 
7.1%
h 2817
 
6.7%
o 2446
 
5.9%
c 2311
 
5.5%
e 2218
 
5.3%
a 2210
 
5.3%
l 1918
 
4.6%
g 1820
 
4.4%
Other values (42) 15661
37.5%
Common
ValueCountFrequency (%)
/ 4504
49.8%
. 1146
 
12.7%
: 1126
 
12.4%
- 1106
 
12.2%
_ 429
 
4.7%
2 196
 
2.2%
0 152
 
1.7%
1 126
 
1.4%
9 54
 
0.6%
3 47
 
0.5%
Other values (5) 162
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 4504
 
8.9%
t 4414
 
8.7%
i 3038
 
6.0%
s 2952
 
5.8%
h 2817
 
5.5%
o 2446
 
4.8%
c 2311
 
4.5%
e 2218
 
4.4%
a 2210
 
4.3%
l 1918
 
3.8%
Other values (57) 22025
43.3%

Project Landscape Category
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Stars
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct160
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.66252
Minimum0
Maximum40001
Zeros44
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:44.862360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q316.75
95-th percentile200.75
Maximum40001
Range40001
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation1502.7525
Coefficient of variation (CV)12.454178
Kurtosis512.5955
Mean120.66252
Median Absolute Deviation (MAD)2
Skewness21.549624
Sum135866
Variance2258265.2
MonotonicityDecreasing
2023-10-16T08:06:44.938601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 316
28.1%
2 130
 
11.5%
3 88
 
7.8%
4 66
 
5.9%
0 44
 
3.9%
5 39
 
3.5%
6 28
 
2.5%
8 24
 
2.1%
7 20
 
1.8%
9 19
 
1.7%
Other values (150) 352
31.3%
ValueCountFrequency (%)
0 44
 
3.9%
1 316
28.1%
2 130
11.5%
3 88
 
7.8%
4 66
 
5.9%
5 39
 
3.5%
6 28
 
2.5%
7 20
 
1.8%
8 24
 
2.1%
9 19
 
1.7%
ValueCountFrequency (%)
40001 1
0.1%
24720 1
0.1%
12603 1
0.1%
12351 1
0.1%
2176 1
0.1%
1952 1
0.1%
1947 1
0.1%
1912 1
0.1%
1609 2
0.2%
1591 1
0.1%

GitHub Forks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct95
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.596803
Minimum0
Maximum16798
Zeros434
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:45.012428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile55
Maximum16798
Range16798
Interquartile range (IQR)6

Descriptive statistics

Standard deviation574.12105
Coefficient of variation (CV)14.499177
Kurtosis691.58875
Mean39.596803
Median Absolute Deviation (MAD)1
Skewness25.300073
Sum44586
Variance329614.98
MonotonicityNot monotonic
2023-10-16T08:06:45.086505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 434
38.5%
1 168
 
14.9%
2 103
 
9.1%
3 64
 
5.7%
4 45
 
4.0%
6 30
 
2.7%
5 26
 
2.3%
8 23
 
2.0%
7 18
 
1.6%
9 18
 
1.6%
Other values (85) 197
17.5%
ValueCountFrequency (%)
0 434
38.5%
1 168
 
14.9%
2 103
 
9.1%
3 64
 
5.7%
4 45
 
4.0%
5 26
 
2.3%
6 30
 
2.7%
7 18
 
1.6%
8 23
 
2.0%
9 18
 
1.6%
ValueCountFrequency (%)
16798 1
0.1%
8644 1
0.1%
3094 1
0.1%
1332 1
0.1%
1154 1
0.1%
850 1
0.1%
535 2
0.2%
475 1
0.1%
471 1
0.1%
422 1
0.1%

GitHub Watchers
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct46
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2886323
Minimum0
Maximum1121
Zeros52
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:45.160321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile15
Maximum1121
Range1121
Interquartile range (IQR)3

Descriptive statistics

Standard deviation39.998302
Coefficient of variation (CV)6.3604135
Kurtosis583.16569
Mean6.2886323
Median Absolute Deviation (MAD)1
Skewness22.764991
Sum7081
Variance1599.8642
MonotonicityNot monotonic
2023-10-16T08:06:45.229542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 362
32.1%
2 247
21.9%
3 117
 
10.4%
4 74
 
6.6%
0 52
 
4.6%
5 48
 
4.3%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
Other values (36) 112
 
9.9%
ValueCountFrequency (%)
0 52
 
4.6%
1 362
32.1%
2 247
21.9%
3 117
 
10.4%
4 74
 
6.6%
5 48
 
4.3%
6 37
 
3.3%
7 34
 
3.0%
8 24
 
2.1%
9 19
 
1.7%
ValueCountFrequency (%)
1121 1
0.1%
595 1
0.1%
351 1
0.1%
156 1
0.1%
95 1
0.1%
86 1
0.1%
78 1
0.1%
54 1
0.1%
51 2
0.2%
49 1
0.1%

GitHub Open Issues
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.023091
Minimum0
Maximum3902
Zeros675
Zeros (%)59.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:45.298585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile33
Maximum3902
Range3902
Interquartile range (IQR)2

Descriptive statistics

Standard deviation181.18356
Coefficient of variation (CV)9.5244021
Kurtosis348.40907
Mean19.023091
Median Absolute Deviation (MAD)0
Skewness17.798596
Sum21420
Variance32827.484
MonotonicityNot monotonic
2023-10-16T08:06:45.374784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 675
59.9%
1 125
 
11.1%
2 51
 
4.5%
3 34
 
3.0%
4 27
 
2.4%
6 27
 
2.4%
8 15
 
1.3%
5 13
 
1.2%
7 12
 
1.1%
19 9
 
0.8%
Other values (70) 138
 
12.3%
ValueCountFrequency (%)
0 675
59.9%
1 125
 
11.1%
2 51
 
4.5%
3 34
 
3.0%
4 27
 
2.4%
5 13
 
1.2%
6 27
 
2.4%
7 12
 
1.1%
8 15
 
1.3%
9 7
 
0.6%
ValueCountFrequency (%)
3902 1
0.1%
3640 1
0.1%
2196 1
0.1%
1020 1
0.1%
905 1
0.1%
701 1
0.1%
555 2
0.2%
422 1
0.1%
358 1
0.1%
321 1
0.1%

GitHub Contributors
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7575488
Minimum0
Maximum435
Zeros27
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:45.446833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile10
Maximum435
Range435
Interquartile range (IQR)2

Descriptive statistics

Standard deviation25.675043
Coefficient of variation (CV)5.3966956
Kurtosis182.39928
Mean4.7575488
Median Absolute Deviation (MAD)0
Skewness12.845713
Sum5357
Variance659.20783
MonotonicityNot monotonic
2023-10-16T08:06:45.511951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 659
58.5%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
0 27
 
2.4%
6 16
 
1.4%
7 11
 
1.0%
10 10
 
0.9%
11 10
 
0.9%
Other values (28) 59
 
5.2%
ValueCountFrequency (%)
0 27
 
2.4%
1 659
58.5%
2 143
 
12.7%
3 102
 
9.1%
4 60
 
5.3%
5 29
 
2.6%
6 16
 
1.4%
7 11
 
1.0%
8 9
 
0.8%
9 8
 
0.7%
ValueCountFrequency (%)
435 1
0.1%
411 1
0.1%
366 1
0.1%
280 1
0.1%
253 1
0.1%
189 1
0.1%
150 1
0.1%
123 2
0.2%
83 1
0.1%
79 1
0.1%

GitHub License Type
Categorical

MISSING 

Distinct15
Distinct (%)2.6%
Missing551
Missing (%)48.9%
Memory size8.9 KiB
MIT
211 
GPL-3.0
164 
NOASSERTION
73 
BSD-3-Clause
51 
Apache-2.0
29 
Other values (10)
47 

Length

Max length18
Median length12
Mean length6.76
Min length3

Characters and Unicode

Total characters3887
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.7%

Sample

1st rowBSD-3-Clause
2nd rowBSD-3-Clause
3rd rowApache-2.0
4th rowMIT
5th rowApache-2.0

Common Values

ValueCountFrequency (%)
MIT 211
 
18.7%
GPL-3.0 164
 
14.6%
NOASSERTION 73
 
6.5%
BSD-3-Clause 51
 
4.5%
Apache-2.0 29
 
2.6%
CC0-1.0 13
 
1.2%
AGPL-3.0 9
 
0.8%
BSD-2-Clause 7
 
0.6%
GPL-2.0 7
 
0.6%
LGPL-3.0 4
 
0.4%
Other values (5) 7
 
0.6%
(Missing) 551
48.9%

Length

2023-10-16T08:06:45.581531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mit 211
36.7%
gpl-3.0 164
28.5%
noassertion 73
 
12.7%
bsd-3-clause 51
 
8.9%
apache-2.0 29
 
5.0%
cc0-1.0 13
 
2.3%
agpl-3.0 9
 
1.6%
bsd-2-clause 7
 
1.2%
gpl-2.0 7
 
1.2%
lgpl-3.0 4
 
0.7%
Other values (5) 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
- 353
 
9.1%
I 284
 
7.3%
T 284
 
7.3%
0 244
 
6.3%
. 231
 
5.9%
3 229
 
5.9%
M 212
 
5.5%
S 205
 
5.3%
L 189
 
4.9%
P 185
 
4.8%
Other values (26) 1471
37.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2310
59.4%
Decimal Number 534
 
13.7%
Lowercase Letter 459
 
11.8%
Dash Punctuation 353
 
9.1%
Other Punctuation 231
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 284
12.3%
T 284
12.3%
M 212
9.2%
S 205
8.9%
L 189
8.2%
P 185
8.0%
G 184
8.0%
N 146
6.3%
O 146
6.3%
A 112
 
4.8%
Other values (7) 363
15.7%
Lowercase Letter
ValueCountFrequency (%)
e 91
19.8%
a 89
19.4%
l 61
13.3%
s 61
13.3%
u 59
12.9%
c 31
 
6.8%
h 29
 
6.3%
p 29
 
6.3%
i 3
 
0.7%
r 2
 
0.4%
Other values (2) 4
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 244
45.7%
3 229
42.9%
2 45
 
8.4%
1 13
 
2.4%
4 3
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 353
100.0%
Other Punctuation
ValueCountFrequency (%)
. 231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2769
71.2%
Common 1118
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 284
 
10.3%
T 284
 
10.3%
M 212
 
7.7%
S 205
 
7.4%
L 189
 
6.8%
P 185
 
6.7%
G 184
 
6.6%
N 146
 
5.3%
O 146
 
5.3%
A 112
 
4.0%
Other values (19) 822
29.7%
Common
ValueCountFrequency (%)
- 353
31.6%
0 244
21.8%
. 231
20.7%
3 229
20.5%
2 45
 
4.0%
1 13
 
1.2%
4 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 353
 
9.1%
I 284
 
7.3%
T 284
 
7.3%
0 244
 
6.3%
. 231
 
5.9%
3 229
 
5.9%
M 212
 
5.5%
S 205
 
5.3%
L 189
 
4.9%
P 185
 
4.8%
Other values (26) 1471
37.8%

GitHub Description
Text

MISSING 

Distinct1057
Distinct (%)98.6%
Missing54
Missing (%)4.8%
Memory size8.9 KiB
2023-10-16T08:06:45.763716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10997
Median length343
Mean length134.00653
Min length7

Characters and Unicode

Total characters143655
Distinct characters111
Distinct categories16 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)97.2%

Sample

1st rowFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
2nd rowThe fundamental package for scientific computing with Python.
3rd rowApache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
4th rowDuckDB is an in-process SQL OLAP Database Management System
5th rowApache Parquet
ValueCountFrequency (%)
analysis 954
 
4.7%
of 791
 
3.9%
and 673
 
3.3%
the 671
 
3.3%
for 655
 
3.2%
cell 518
 
2.6%
single-cell 470
 
2.3%
data 453
 
2.2%
single 397
 
2.0%
a 367
 
1.8%
Other values (4026) 14207
70.5%
2023-10-16T08:06:46.052644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19231
13.4%
e 12713
 
8.8%
a 9950
 
6.9%
i 9848
 
6.9%
n 8865
 
6.2%
s 8753
 
6.1%
l 8246
 
5.7%
o 7935
 
5.5%
t 7907
 
5.5%
r 6172
 
4.3%
Other values (101) 44035
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111888
77.9%
Space Separator 19270
 
13.4%
Uppercase Letter 7164
 
5.0%
Other Punctuation 2203
 
1.5%
Dash Punctuation 1309
 
0.9%
Decimal Number 1209
 
0.8%
Close Punctuation 235
 
0.2%
Open Punctuation 229
 
0.2%
Math Symbol 52
 
< 0.1%
Final Punctuation 40
 
< 0.1%
Other values (6) 56
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12713
11.4%
a 9950
 
8.9%
i 9848
 
8.8%
n 8865
 
7.9%
s 8753
 
7.8%
l 8246
 
7.4%
o 7935
 
7.1%
t 7907
 
7.1%
r 6172
 
5.5%
c 4977
 
4.4%
Other values (19) 26522
23.7%
Uppercase Letter
ValueCountFrequency (%)
A 1115
15.6%
S 778
10.9%
R 736
10.3%
C 707
9.9%
N 605
 
8.4%
T 409
 
5.7%
D 360
 
5.0%
P 330
 
4.6%
I 320
 
4.5%
M 290
 
4.0%
Other values (16) 1514
21.1%
Other Punctuation
ValueCountFrequency (%)
. 912
41.4%
, 625
28.4%
: 187
 
8.5%
/ 177
 
8.0%
" 169
 
7.7%
% 40
 
1.8%
' 36
 
1.6%
; 18
 
0.8%
& 14
 
0.6%
@ 7
 
0.3%
Other values (4) 18
 
0.8%
Other Symbol
ValueCountFrequency (%)
6
37.5%
🐟 1
 
6.2%
🏔 1
 
6.2%
🌍 1
 
6.2%
🍱 1
 
6.2%
🍣 1
 
6.2%
🧬 1
 
6.2%
🦀 1
 
6.2%
🌸 1
 
6.2%
🧫 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 264
21.8%
0 255
21.1%
1 211
17.5%
3 94
 
7.8%
9 88
 
7.3%
7 70
 
5.8%
5 70
 
5.8%
4 64
 
5.3%
8 48
 
4.0%
6 45
 
3.7%
Math Symbol
ValueCountFrequency (%)
= 22
42.3%
+ 15
28.8%
> 10
19.2%
< 4
 
7.7%
~ 1
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 1305
99.7%
2
 
0.2%
2
 
0.2%
Space Separator
ValueCountFrequency (%)
19231
99.8%
  39
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 232
98.7%
] 3
 
1.3%
Open Punctuation
ValueCountFrequency (%)
( 226
98.7%
[ 3
 
1.3%
Final Punctuation
ValueCountFrequency (%)
32
80.0%
8
 
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Initial Punctuation
ValueCountFrequency (%)
8
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119051
82.9%
Common 24601
 
17.1%
Greek 1
 
< 0.1%
Inherited 1
 
< 0.1%
Han 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
19231
78.2%
- 1305
 
5.3%
. 912
 
3.7%
, 625
 
2.5%
2 264
 
1.1%
0 255
 
1.0%
) 232
 
0.9%
( 226
 
0.9%
1 211
 
0.9%
: 187
 
0.8%
Other values (44) 1153
 
4.7%
Latin
ValueCountFrequency (%)
e 12713
 
10.7%
a 9950
 
8.4%
i 9848
 
8.3%
n 8865
 
7.4%
s 8753
 
7.4%
l 8246
 
6.9%
o 7935
 
6.7%
t 7907
 
6.6%
r 6172
 
5.2%
c 4977
 
4.2%
Other values (44) 33685
28.3%
Greek
ValueCountFrequency (%)
α 1
100.0%
Inherited
ValueCountFrequency (%)
1
100.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143541
99.9%
None 54
 
< 0.1%
Punctuation 52
 
< 0.1%
Geometric Shapes 6
 
< 0.1%
VS 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19231
13.4%
e 12713
 
8.9%
a 9950
 
6.9%
i 9848
 
6.9%
n 8865
 
6.2%
s 8753
 
6.1%
l 8246
 
5.7%
o 7935
 
5.5%
t 7907
 
5.5%
r 6172
 
4.3%
Other values (79) 43921
30.6%
None
ValueCountFrequency (%)
  39
72.2%
ü 3
 
5.6%
α 1
 
1.9%
🐟 1
 
1.9%
🏔 1
 
1.9%
🌍 1
 
1.9%
🍱 1
 
1.9%
🍣 1
 
1.9%
🧬 1
 
1.9%
é 1
 
1.9%
Other values (4) 4
 
7.4%
Punctuation
ValueCountFrequency (%)
32
61.5%
8
 
15.4%
8
 
15.4%
2
 
3.8%
2
 
3.8%
Geometric Shapes
ValueCountFrequency (%)
6
100.0%
VS
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

GitHub Topics
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

GitHub Detected Languages
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB
Distinct1123
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2010-08-24 01:37:33+00:00
Maximum2023-10-10 14:24:36+00:00
2023-10-16T08:06:46.148077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:46.220053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1123
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2011-06-21 13:44:00+00:00
Maximum2023-10-13 20:03:44+00:00
2023-10-16T08:06:46.289508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:46.366257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Duration Created to Most Recent Commit
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Created to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

Repository Size (KB)
Real number (ℝ)

Distinct1011
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75387.096
Minimum1
Maximum1922901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:46.443511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1690
median10750
Q365061.5
95-th percentile360071.5
Maximum1922901
Range1922900
Interquartile range (IQR)64371.5

Descriptive statistics

Standard deviation185888.96
Coefficient of variation (CV)2.4657928
Kurtosis40.079747
Mean75387.096
Median Absolute Deviation (MAD)10709.5
Skewness5.5510527
Sum84885870
Variance3.4554705 × 1010
MonotonicityNot monotonic
2023-10-16T08:06:46.609217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 10
 
0.9%
26 7
 
0.6%
13 6
 
0.5%
3 6
 
0.5%
24 6
 
0.5%
67 5
 
0.4%
16 5
 
0.4%
19 4
 
0.4%
17 4
 
0.4%
22 4
 
0.4%
Other values (1001) 1069
94.9%
ValueCountFrequency (%)
1 2
 
0.2%
2 2
 
0.2%
3 6
0.5%
4 3
 
0.3%
5 3
 
0.3%
6 10
0.9%
7 3
 
0.3%
8 3
 
0.3%
9 3
 
0.3%
10 1
 
0.1%
ValueCountFrequency (%)
1922901 1
0.1%
1921765 1
0.1%
1792729 1
0.1%
1590023 1
0.1%
1496132 1
0.1%
1461986 1
0.1%
1447466 1
0.1%
1293566 1
0.1%
1245051 1
0.1%
1068214 1
0.1%

GitHub Repo Archived
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
False
1113 
True
 
13
ValueCountFrequency (%)
False 1113
98.8%
True 13
 
1.2%
2023-10-16T08:06:46.674207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Negative Duration Most Recent Commit to Now
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size8.9 KiB

category
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
related-tools-github-query-result
1007 
cytomining-ecosystem-adjacent-tools
 
100
cytomining-ecosystem-relevant-open-source
 
8
microscopy-analysis-tools
 
7
loi-focus
 
4

Length

Max length41
Median length33
Mean length33.099467
Min length9

Characters and Unicode

Total characters37270
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcytomining-ecosystem-relevant-open-source
2nd rowcytomining-ecosystem-relevant-open-source
3rd rowcytomining-ecosystem-relevant-open-source
4th rowcytomining-ecosystem-relevant-open-source
5th rowcytomining-ecosystem-relevant-open-source

Common Values

ValueCountFrequency (%)
related-tools-github-query-result 1007
89.4%
cytomining-ecosystem-adjacent-tools 100
 
8.9%
cytomining-ecosystem-relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Length

2023-10-16T08:06:46.729529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T08:06:46.784781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
related-tools-github-query-result 1007
89.4%
cytomining-ecosystem-adjacent-tools 100
 
8.9%
cytomining-ecosystem-relevant-open-source 8
 
0.7%
microscopy-analysis-tools 7
 
0.6%
loi-focus 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 4459
12.0%
- 4378
11.7%
e 4376
11.7%
l 3147
8.4%
r 3044
8.2%
u 3033
8.1%
o 2482
 
6.7%
s 2370
 
6.4%
i 1241
 
3.3%
y 1237
 
3.3%
Other values (13) 7503
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32892
88.3%
Dash Punctuation 4378
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4459
13.6%
e 4376
13.3%
l 3147
9.6%
r 3044
9.3%
u 3033
9.2%
o 2482
 
7.5%
s 2370
 
7.2%
i 1241
 
3.8%
y 1237
 
3.8%
a 1229
 
3.7%
Other values (12) 6274
19.1%
Dash Punctuation
ValueCountFrequency (%)
- 4378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32892
88.3%
Common 4378
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4459
13.6%
e 4376
13.3%
l 3147
9.6%
r 3044
9.3%
u 3033
9.2%
o 2482
 
7.5%
s 2370
 
7.2%
i 1241
 
3.8%
y 1237
 
3.8%
a 1229
 
3.7%
Other values (12) 6274
19.1%
Common
ValueCountFrequency (%)
- 4378
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4459
12.0%
- 4378
11.7%
e 4376
11.7%
l 3147
8.4%
r 3044
8.2%
u 3033
8.1%
o 2482
 
6.7%
s 2370
 
6.4%
i 1241
 
3.3%
y 1237
 
3.3%
Other values (13) 7503
20.1%
Distinct917
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8485316
Minimum0.01369863
Maximum13.153425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-10-16T08:06:46.853787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.01369863
5-th percentile0.64178082
Q12.2369863
median3.5424658
Q35.2883562
95-th percentile7.8075342
Maximum13.153425
Range13.139726
Interquartile range (IQR)3.0513699

Descriptive statistics

Standard deviation2.2611527
Coefficient of variation (CV)0.58753647
Kurtosis0.81371944
Mean3.8485316
Median Absolute Deviation (MAD)1.4780822
Skewness0.75989833
Sum4333.4466
Variance5.1128114
MonotonicityNot monotonic
2023-10-16T08:06:46.930367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.15890411 4
 
0.4%
3.775342466 4
 
0.4%
2.695890411 4
 
0.4%
1.97260274 3
 
0.3%
2.410958904 3
 
0.3%
5.345205479 3
 
0.3%
2.043835616 3
 
0.3%
3.21369863 3
 
0.3%
0.3945205479 3
 
0.3%
3.315068493 3
 
0.3%
Other values (907) 1093
97.1%
ValueCountFrequency (%)
0.01369863014 1
0.1%
0.09315068493 1
0.1%
0.09589041096 1
0.1%
0.1452054795 1
0.1%
0.1506849315 1
0.1%
0.1643835616 1
0.1%
0.1698630137 1
0.1%
0.1726027397 1
0.1%
0.1753424658 2
0.2%
0.2219178082 2
0.2%
ValueCountFrequency (%)
13.15342466 1
0.1%
13.09589041 1
0.1%
13.07671233 1
0.1%
12.53972603 1
0.1%
12.21643836 1
0.1%
12.1369863 1
0.1%
12.07671233 1
0.1%
11.28767123 1
0.1%
11.09863014 1
0.1%
10.60821918 1
0.1%

Interactions

2023-10-16T08:06:42.393082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:39.857284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.373891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.835708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.214248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.617639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.986581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.451326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:39.962291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.433324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.892491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.274541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.673880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.049809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.506156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.062727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.488227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.947485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.330602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.725336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.109280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.556000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.147586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.608536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.998672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.384366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.775939image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.164353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.612859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.205781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.666786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.053753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.443392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.831532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.225067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.663504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.257929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.719656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.101685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.499764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.878462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.277352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.723138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.319252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:40.781292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.159998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.561886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:41.934263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-10-16T08:06:42.337526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-10-16T08:06:46.984414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
GitHub StarsGitHub ForksGitHub WatchersGitHub Open IssuesGitHub ContributorsRepository Size (KB)Duration Created to Now in YearsGitHub License TypeGitHub Repo Archivedcategory
GitHub Stars1.0000.8200.6460.6890.5390.3990.3700.0000.0000.404
GitHub Forks0.8201.0000.6300.6660.5560.3820.4190.0000.0000.348
GitHub Watchers0.6460.6301.0000.5920.5620.3490.4500.0000.0000.348
GitHub Open Issues0.6890.6660.5921.0000.5520.2860.3120.0000.0000.358
GitHub Contributors0.5390.5560.5620.5521.0000.3940.2530.0000.0000.482
Repository Size (KB)0.3990.3820.3490.2860.3941.0000.1430.0000.0000.046
Duration Created to Now in Years0.3700.4190.4500.3120.2530.1431.0000.1030.1010.297
GitHub License Type0.0000.0000.0000.0000.0000.0000.1031.0000.0000.227
GitHub Repo Archived0.0000.0000.0000.0000.0000.0000.1010.0001.0000.000
category0.4040.3480.3480.3580.4820.0460.2970.2270.0001.000

Missing values

2023-10-16T08:06:42.813492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-16T08:06:42.971124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-16T08:06:43.071143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Project NameProject HomepageProject Repo URLProject Landscape CategoryGitHub StarsGitHub ForksGitHub WatchersGitHub Open IssuesGitHub ContributorsGitHub License TypeGitHub DescriptionGitHub TopicsGitHub Detected LanguagesDate CreatedDate Most Recent CommitDuration Created to Most Recent CommitDuration Created to NowDuration Most Recent Commit to NowRepository Size (KB)GitHub Repo ArchivedNegative Duration Most Recent Commit to NowcategoryDuration Created to Now in Years
0pandashttps://pandas.pydata.orghttps://github.com/pandas-dev/pandas[cytomining-ecosystem-relevant-open-source]400011679811213640411BSD-3-ClauseFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more[alignment, data-analysis, data-science, flexible, pandas, python]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 386224.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 6804.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1277471.0, 'D': None, 'Dockerfile': 5751.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 457000.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': 10664.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 20322774.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 14392.0, 'Singularity': None, 'Smarty': 8486.0, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': 1196.0, 'eC': None, 'sed': None}2010-08-24 01:37:33+00:002023-10-13 20:03:44+00:004798 days 18:26:114801 days 12:29:04.4788350 days 00:00:19.235293334934False-1 days +23:59:40.764707cytomining-ecosystem-relevant-open-source13.153425
1numpyhttps://numpy.orghttps://github.com/numpy/numpy[cytomining-ecosystem-relevant-open-source]2472086445952196435BSD-3-ClauseThe fundamental package for scientific computing with Python.[numpy, python]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 6220070.0, 'C#': None, 'C++': 205725.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 151476.0, 'D': 19.0, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': 3787.0, 'Fortran': 27683.0, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 1697.0, 'Mako': None, 'Mercury': None, 'Meson': 88875.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 10457640.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 17058.0, 'Singularity': None, 'Smarty': 4129.0, 'Stan': None, 'Standard ML': None, 'Starlark': 1842.0, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 5699.0}2010-09-13 23:02:39+00:002023-10-13 19:01:51+00:004777 days 19:59:124780 days 15:03:58.4788350 days 01:02:12.235293131862False-1 days +22:57:47.764707cytomining-ecosystem-relevant-open-source13.095890
2arrowhttps://arrow.apache.org/https://github.com/apache/arrow[cytomining-ecosystem-relevant-open-source]1260330943513902366Apache-2.0Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing[arrow]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 3709.0, 'Batchfile': 32824.0, 'C': 1507496.0, 'C#': 1505684.0, 'C++': 26858030.0, 'CMake': 732369.0, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1708430.0, 'D': None, 'Dockerfile': 135006.0, 'Emacs Lisp': 1064.0, 'Forth': None, 'Fortran': None, 'FreeMarker': 2312.0, 'Gnuplot': None, 'Go': 5619866.0, 'Groovy': None, 'HCL': None, 'HTML': 5604.0, 'Hack': None, 'ImageJ Macro': None, 'Java': 7353737.0, 'JavaScript': 128685.0, 'Jinja': 21888.0, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': 8771.0, 'M': None, 'M4': None, 'MATLAB': 722935.0, 'Makefile': 32659.0, 'Mako': None, 'Mercury': None, 'Meson': 62865.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': 11472.0, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 3288210.0, 'QMake': None, 'R': 1698163.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': 1794908.0, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 411223.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 461913.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 34537.0, 'TypeScript': 1108325.0, 'VBScript': None, 'Vala': 24798.0, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 1256.0}2016-02-17 08:00:23+00:002023-10-13 17:57:49+00:002795 days 09:57:262798 days 06:06:14.4788350 days 02:06:14.235293170891False-1 days +21:53:45.764707cytomining-ecosystem-relevant-open-source7.665753
3duckdbhttp://www.duckdb.orghttps://github.com/duckdb/duckdb[cytomining-ecosystem-relevant-open-source]123511154156321253MITDuckDB is an in-process SQL OLAP Database Management System[analytics, database, embedded-database, olap, sql]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1761733.0, 'C#': None, 'C++': 33575975.0, 'CMake': 146234.0, 'CSS': 182.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 343518.0, 'JavaScript': 12990.0, 'Jinja': None, 'Julia': 250635.0, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 14649.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1407102.0, 'QMake': None, 'R': 1693.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 26187.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 282562.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-06-26 15:04:45+00:002023-10-13 13:57:33+00:001934 days 22:52:481937 days 23:01:52.4788350 days 06:06:30.235293226865False-1 days +17:53:29.764707cytomining-ecosystem-relevant-open-source5.306849
4parquet-mrhttps://github.com/apache/parquet-mr[cytomining-ecosystem-relevant-open-source]2176133295130189Apache-2.0Apache Parquet[big-data, java, parquet]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 5920194.0, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 14771.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': 8436.0, 'Scheme': None, 'Scilab': None, 'Shell': 14860.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 10354.0, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2014-06-10 07:00:07+00:002023-10-13 15:48:32+00:003412 days 08:48:253415 days 07:06:30.4788350 days 04:15:31.23529318492False-1 days +19:44:28.764707cytomining-ecosystem-relevant-open-source9.356164
5snakemakehttps://snakemake.readthedocs.iohttps://github.com/snakemake/snakemake[cytomining-ecosystem-relevant-open-source]195247521905280MITThis is the development home of the workflow management system Snakemake. For general information, see[reproducibility, snakemake, workflow-management]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1346.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 3033.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 1727.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 3647966.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': 43973.0, 'Jinja': 6950.0, 'Julia': 334.0, 'Jupyter Notebook': 4389.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 4400.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': 5.0, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1341162.0, 'QMake': None, 'R': 786.0, 'Raku': None, 'Reason': None, 'Rebol': 6.0, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': 6.0, 'Ruby': None, 'Rust': 3617.0, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 5149.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': 5722.0, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2019-10-04 14:58:11+00:002023-10-13 13:52:54+00:001469 days 22:54:431472 days 23:08:26.4788350 days 06:11:09.23529391896False-1 days +17:48:50.764707cytomining-ecosystem-relevant-open-source4.032877
6seurathttp://www.satijalab.org/seurathttps://github.com/satijalab/seurat[cytomining-ecosystem-adjacent-tools]19478507822683NOASSERTIONR toolkit for single cell genomics[cran, human-cell-atlas, single-cell-genomics, single-cell-rna-seq]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 166.0, 'C#': None, 'C++': 103887.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 1272988.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 942.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2015-05-20 05:23:02+00:002023-10-03 21:28:12+00:003058 days 16:05:103071 days 08:43:35.4788359 days 22:35:51.23529322722False-10 days +01:24:08.764707cytomining-ecosystem-adjacent-tools8.413699
7naparihttps://napari.orghttps://github.com/napari/napari[microscopy-analysis-tools]1912389491020150BSD-3-Clausenapari: a fast, interactive, multi-dimensional image viewer for python[napari, numpy, python, visualization]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 465.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 6722.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 4637489.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': 2846.0, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 1221.0, 'Singularity': 95.0, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-08-13 01:12:28+00:002023-10-13 19:19:35+00:001887 days 18:07:071890 days 12:54:09.4788350 days 00:44:28.23529376651False-1 days +23:15:31.764707microscopy-analysis-tools5.178082
8scanpyhttps://scanpy.readthedocs.iohttps://github.com/scverse/scanpy[cytomining-ecosystem-adjacent-tools]160953551555123BSD-3-ClauseSingle-cell analysis in Python. Scales to >1M cells.[anndata, bioinformatics, data-science, machine-learning, python, scanpy, scverse, transcriptomics, visualize-data]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1291622.0, 'QMake': None, 'R': 2315.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2017-01-29 11:31:11+00:002023-10-13 13:56:56+00:002448 days 02:25:452451 days 02:35:26.4788350 days 06:07:07.23529339334False-1 days +17:52:52.764707cytomining-ecosystem-adjacent-tools6.715068
9scanpyhttps://scanpy.readthedocs.iohttps://github.com/scverse/scanpy[related-tools-github-query-result]160953551555123BSD-3-ClauseSingle-cell analysis in Python. Scales to >1M cells.[anndata, bioinformatics, data-science, machine-learning, python, scanpy, scverse, transcriptomics, visualize-data]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1291622.0, 'QMake': None, 'R': 2315.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2017-01-29 11:31:11+00:002023-10-13 13:56:56+00:002448 days 02:25:452451 days 02:35:26.4788350 days 06:07:07.23529339334False-1 days +17:52:52.764707related-tools-github-query-result6.715068
Project NameProject HomepageProject Repo URLProject Landscape CategoryGitHub StarsGitHub ForksGitHub WatchersGitHub Open IssuesGitHub ContributorsGitHub License TypeGitHub DescriptionGitHub TopicsGitHub Detected LanguagesDate CreatedDate Most Recent CommitDuration Created to Most Recent CommitDuration Created to NowDuration Most Recent Commit to NowRepository Size (KB)GitHub Repo ArchivedNegative Duration Most Recent Commit to NowcategoryDuration Created to Now in Years
1116BloodVesselImageAnalysishttps://github.com/RoopaMadhu/BloodVesselImageAnalysis[related-tools-github-query-result]00101NoneSpatiographic projections of biological systems[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 188297.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-12-22 12:36:17+00:002020-12-22 14:22:59+00:000 days 01:46:421028 days 01:30:20.4788351025 days 05:41:04.235293576False-1026 days +18:18:55.764707related-tools-github-query-result2.816438
1117SickleCellDataAnalysisNonehttps://github.com/ridz46/SickleCellDataAnalysis[related-tools-github-query-result]00101NoneThis repository contains the codes used for image processing and further downstream data analysis for the project on the development of a point-of-care diagnostic test for sickle cell disease. This repository is maintained by the Microfluidics & Biological Physics group, Department of Biosciences & Bioengineering, Indian Institute of Technology Bombay, Mumbai, India.[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 4373.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-09-25 13:45:27+00:002020-09-28 08:55:24+00:002 days 19:09:571116 days 00:21:10.4788351110 days 11:08:39.23529317False-1111 days +12:51:20.764707related-tools-github-query-result3.057534
1118Data-Analysis-AMATH-482Nonehttps://github.com/priyanshir/Data-Analysis-AMATH-482[related-tools-github-query-result]00101NoneExploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 703573.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 33375.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-03-09 04:17:02+00:002020-03-14 22:41:50+00:005 days 18:24:481316 days 09:49:35.4788351307 days 21:22:13.2352932832False-1308 days +02:37:46.764707related-tools-github-query-result3.605479
1119Aging-Cell-Morphology-Cell-transformations-and-image-processingNonehttps://github.com/sumisingh/Aging-Cell-Morphology-Cell-transformations-and-image-processing[related-tools-github-query-result]00101NoneIdentifying cellular transformations associated with aging using image processing[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 8130261.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2020-01-20 05:18:57+00:002020-01-20 05:29:56+00:000 days 00:10:591365 days 08:47:40.4788351362 days 14:34:07.23529327874False-1363 days +09:25:52.764707related-tools-github-query-result3.739726
1120CellMorphologyhttps://github.com/KnightofDawn/CellMorphology[related-tools-github-query-result]00101NonePython code to identify/analyze cells from microscopic stack images.[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 65906.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-09-11 04:25:09+00:002018-09-10 20:41:28+00:00-1 days +16:16:191861 days 09:41:28.4788351858 days 23:22:35.235293161False-1859 days +00:37:24.764707related-tools-github-query-result5.098630
1121ImagingCellsNonehttps://github.com/jesnyder/ImagingCells[related-tools-github-query-result]00101NoneScripts to analyze cell number and morphologies using images.[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 1523.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-06-15 19:50:00+00:002018-08-31 19:21:33+00:0076 days 23:31:331948 days 18:16:37.4788351869 days 00:42:30.2352931False-1870 days +23:17:29.764707related-tools-github-query-result5.336986
1122course-biaNonehttps://github.com/denzf/course-bia[related-tools-github-query-result]00101MITCode examples for the course of Biological Image Analysis[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 6010.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-01-28 21:58:13+00:002018-01-24 03:22:19+00:00-5 days +05:24:062086 days 16:08:24.4788352088 days 16:41:44.235293203False-2089 days +07:18:15.764707related-tools-github-query-result5.715068
1123Cell-virulence-Detection-using-Image-ProcessingNonehttps://github.com/arushigupta148/Cell-virulence-Detection-using-Image-Processing[related-tools-github-query-result]00001NoneDesigned an automated tool to find the thickness of multiple cell capsules from images using morphological operations to generate plots of cell size vs capsular thickness, simplifying detection of virulence in yeast cells for mycologists[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 12989.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-12-27 08:27:06+00:002019-05-12 23:09:02+00:00136 days 14:41:561754 days 05:39:31.4788351614 days 20:55:01.2352931751False-1615 days +03:04:58.764707related-tools-github-query-result4.805479
1124Image-analysishttps://github.com/dguin/Image-analysis[related-tools-github-query-result]00001NoneThe repository contains code to analyze videos where each frame is a snapshot of the cellular status as a function of time. The program includes subroutines for segmentation protocols to pick a cell and differentiate it from the background when the signal to noise is low. The protocol docx explains what each code does and explains the order in which they must be run. As is the program analyzes FRET data from a cell, where the temperature increases as a function of time and one can evaluate the changes in the cell morphology as the cell is under heat stress[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 40670.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-10-13 18:53:42+00:002018-10-13 19:32:22+00:000 days 00:38:401828 days 19:12:55.4788351826 days 00:31:41.23529326False-1827 days +23:28:18.764707related-tools-github-query-result5.008219
1125oct-image-analysishttps://github.com/ricster101/oct-image-analysis[related-tools-github-query-result]00001NoneWork developed with Adriana Costa during the course of Computer Vision and Biological Perception aiming to discover differences in mice retina[]{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 22755.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}2018-03-19 01:29:32+00:002018-03-19 03:31:25+00:000 days 02:01:532037 days 12:37:05.4788352034 days 16:32:38.23529316False-2035 days +07:27:21.764707related-tools-github-query-result5.580822